The Isometric mapping algorithm is an unsupervised manifold learning algorithm, with no consideration of the class of training samples, while supervised isometric mapping treats the difference among classes equally. Considering the inner relationship between different expressions, we have proposed isometric mapping algorithm based on expression weighted distance, which assigns weighted values a...

Significant improvements in face-recognition performance have recently been achieved by obtaining near infrared (NIR) probe images. We demonstrate that by taking into account the differential effects of sub-surface scattering, correlation between facial images in the visible (VIS) and NIR wavelengths can be significantly improved. Hence, by using Fourier analysis and Gaussian deconvolution with...

In the past decade there has been a resurgence of interest in nonlinear dimension reduction. Among new proposals are “Local Linear Embedding” (LLE, Roweis and Saul 2000), “Isomap” (Tenenbaum et al. 2000) and Kernel PCA (KPCA, Schölkopf, Smola and Müller 1998), which all construct global lowdimensional embeddings from local affine or metric information. We introduce a competing method called “Lo...

Fluorescence imaging has been demonstrated to be able to detect and diagnose different tumour types in a range of biological tissues including cervix, lung, and along the gastrointestinal tract. An approach based on laser induced tissue autofluorescence may be applied minimally invasively using endoscopes, although there is a high degree of overlap in endogenous fluorophore excitation/emission ...

In this paper, we propose a method to cluster multiple intersected manifolds. The algorithm chooses several landmark nodes randomly and then checks whether there is an angle-constrained path between each landmark node and every other node in the neighborhood graph. When the points lie on different manifolds with intersection they should not be connected using a smooth path, thus the angle const...

Spectral dimensionality reduction is frequently used to identify low-dimensional structure in high-dimensional data. However, learning manifolds, especially from the streaming data, is computationally and memory expensive. In this paper, we argue that a stable manifold can be learned using only a fraction of the stream, and the remaining stream can be mapped to the manifold in a significantly l...

This paper proposes an inner product Laplacian embedding algorithm based on semi-definite programming, named as IPLE algorithm. The new algorithm learns a geodesic distance-based kernel matrix by using semi-definite programming under the constraints of local contraction. The criterion function is to make the neighborhood points on manifold as close as possible while the geodesic distances betwe...

Studies of the degrees of freedom or “synergies” in musculoskeletal systems rely critically on algorithms to estimate the “dimension” of kinematic or neural data. Linear algorithms such as principal component analysis (PCA) are used almost exclusively for this purpose. However, biological systems tend to possess nonlinearities and operate at multiple spatial and temporal scales so that the set ...

Principal Component Analysis ( PCA ), Locally Linear Embedding ( LLE ) and Isomap techniques can be used to process and analyze high-dimensional data domains. These methodologies create low-dimensional embeddings of the original data which are easier to work with than the initial high-dimensional data. The goal of this report is to show how the above methods can be applied to very high-dimensio...

This study was carried out for rapid and noninvasive determination of the class of sorghum species by using the manifold dimensionality reduction (MDR) method and the nonlinear regression method of least squares support vector machines (LS-SVM) combing with the mid-infrared spectroscopy (MIRS) techniques. The methods of Durbin and Run test of augmented partial residual plot (APaRP) were perform...